Description:
Due to the presence of heterogeneous sensing modalities
(eg. vision, audio and inertial) and different deployment
scenarios, edge devices cannot use same machine learning
model. However, training separate models are limited
by the availability of labeled data. To address
the challenge, inspired from biological sensory
substitution such as touch to sight, we explore the idea
of sensory substitution in edge devices. To enable sensory
substitution, our approach is to learn a shared representation
using unlabeled data across modalities. We
exploit the fact that in the same environment, edge devices
are capturing the same event. Our evaluation using
human activity recognition as a use case shows that sensory
substitution can reduce the required labeled data by
up to 90% and can speed up the training process by up
to 50 times in comparison to training edge devices from
scratch.